Wednesday, 16 November 2016

The accuracy of stereotypes

Are immigrants more likely to claim benefits, or is this a stereotype?

A stereotype is a preliminary insight. A stereotype can be true, the first step in noticing differences. For conceptual economy, stereotypes encapsulate the characteristics most people have noticed. Not all heuristics are false.

This study is interesting, in that it was pre-registered, so its absence would have been noticed. It compares stereotypes against actual data to get a test of accuracy. I was particularly struck by how the authors studied the answers at each wave of data collection, and tracked down those who gave perplexing answers, then refining their survey questions to reduce misunderstandings.

The paper also points out an unremarked aspect of stereotypes: they may be too weak. Stereotypes have to show a correlation with the facts, and be good predictors. You have to get the slope right, and also the intercept. It is not enough to have a vague notion that immigrants are on benefits, you ought to be able to estimate how many are on benefits. A stronger stereotype would be a more accurate perception of reality.

A nationally representative Danish sample was asked to estimate the percentage of persons aged 30-39 living in Denmark receiving social benefits for 70 countries of origin (N = 766). After extensive quality control procedures, a sample of 484 persons were available for analysis. Stereotypes were scored by accuracy by comparing the estimates values to values obtained from an official source. Individual stereotypes were found to be fairly accurate (median/mean correlation with criterion values = .48/.43), while the aggregate stereotype was found to be very accurate (r = .70). Both individual and aggregate-level stereotypes tended to underestimate the percentages of persons receiving social benefits and underestimate real group differences.In bivariate analysis, stereotype correlational accuracy was found to be predicted by a variety of predictors at above chance levels, including conservatism (r = .13), nationalism (r = .11), some immigration critical beliefs/preferences, agreement with a few political parties, educational attainment (r = .20), being male (d = .19) and cognitive ability (r = .22). Agreement with most political parties, experience with ghettos, age, and policy positions on immigrant questions had little or no predictive validity.In multivariate predictive analysis using LASSO regression, correlational accuracy was found to be predicted only by cognitive ability and educational attainment with even moderate level of reliability. In general, stereotype accuracy was not easy to predict, even using 24 predictors (k-fold cross-validated R2 = 4%).We examined whether stereotype accuracy was related to the proportion of Muslims in the groups. Stereotypes were found to be less accurate for the groups with higher proportions of Muslims in that participants underestimated the percentages of persons receiving social benefits (mean estimation error for Muslim groups relative to overall elevation error = -8.09 %points).The study was preregistered with most analyses being specified before data collection began

The observed correlation of .7 is big, and useful. A majority of immigrants from Syria, Somalia and Kuwait are on benefits, as are those from Iraq and Lebanon. Even more to the point, if the benchmark is 25% for Danish citizens, then there are 19 countries with higher benefit rates. More positively, there are countries with lower rates, presumably because they are younger and employed. The data plot does not give us any guide to numbers from each country. However, later in the paper it is shown that immigrant population size is not relevant in judging benefit rates accurately.

The best predictor of having accurate stereotypes was cognitive ability (81% of simulations), followed by educational attainment (74% of simulations). Respondents underestimate the number of Muslims on benefits.

This is a very good paper. Data handling is exceptional, and well explained. There are lots of Figures and Tables. The sample is large and representative. The results have been looked at carefully, to identify those who participated without paying much attention to the questions. The data are available for re-analysis.

The high accuracy of aggregate stereotypes is confirmed. If anything, the stereotypes held by Danish people about immigrants underestimates those immigrants’ reliance on Danish benefits.

11 comments:

I wonder whether they meant literally immigrants (i.e. those born elsewhere) or "immigrants" as it is commonly used in Britain, to mean those born elsewhere plus those descended from immigrants who have arrived since the The War.

Mind you, the discrepancy between those two uses might be pretty small in Denmark where (I'm guessing) mass Third World immigration might be a fairly recent phenomenon. Using people aged 30-39 might also help keep the meaning distinct.

The IAB data is in http://www.iab.de/en/daten/iab-brain-drain-data.aspx

Previously there were some questions if the Nigerian immigrants in UK were typical of those back in Nigeria. The Nigerians in UK are well up in the top 10 in terms of % degree holders and thus a very self selected group. If %Edu is a proxy for the expected relative IQ of the group, it might not be surprising if the UK Nigerians might be able to perform better than the Chineseor Indians there.

Thanks. Elite performance is a good indicator of underlying average ability, so long as one has an accurate estimate of the size of the population. Nigeria is predicted to rise to 1 billion within a generation, so hard to get precise numbers of what the number of Nigerian are at the moment, I would think

The brain drain data is a nice way of adjusting for emigration selection. The idea is that I do a few more of these large scale country of origin studies. Then do a big meta-analysis where I take into account the emigration selection data too, see how that affects results. I think there will be substantial effects for a few countries such as South Africa.